{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run this in Colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DtyZZfu4qFzE"
   },
   "source": [
    "# Set up AutoGluon. It is involved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 42055,
     "status": "ok",
     "timestamp": 1598793346667,
     "user": {
      "displayName": "Srinivas Chilukuri",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi6umIgrfUrIhlzS_2MLjaQhJAqRigLHF92McBw=s64",
      "userId": "11563496605008272618"
     },
     "user_tz": 300
    },
    "id": "YsoLrowfmYMR",
    "outputId": "daabfaa1-eeb8-4f15-a03e-6b00f56c8a81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uninstalling mkl-2019.0:\n",
      "  Successfully uninstalled mkl-2019.0\n"
     ]
    }
   ],
   "source": [
    "pip uninstall -y mkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53011,
     "status": "ok",
     "timestamp": 1598793357635,
     "user": {
      "displayName": "Srinivas Chilukuri",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi6umIgrfUrIhlzS_2MLjaQhJAqRigLHF92McBw=s64",
      "userId": "11563496605008272618"
     },
     "user_tz": 300
    },
    "id": "g0rM7nLmm0og",
    "outputId": "fc5e0fc8-2220-41c6-8b30-f443670ea5be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mxnet\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/bb/54cbabe428351c06d10903c658878d29ee7026efbe45133fd133598d6eb6/mxnet-1.7.0.post1-py2.py3-none-manylinux2014_x86_64.whl (55.0MB)\n",
      "\u001b[K     |████████████████████████████████| 55.0MB 77kB/s \n",
      "\u001b[?25hCollecting graphviz<0.9.0,>=0.8.1\n",
      "  Downloading https://files.pythonhosted.org/packages/53/39/4ab213673844e0c004bed8a0781a0721a3f6bb23eb8854ee75c236428892/graphviz-0.8.4-py2.py3-none-any.whl\n",
      "Requirement already satisfied, skipping upgrade: requests<3,>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from mxnet) (2.23.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.6/dist-packages (from mxnet) (1.18.5)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet) (1.24.3)\n",
      "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
      "Installing collected packages: graphviz, mxnet\n",
      "  Found existing installation: graphviz 0.10.1\n",
      "    Uninstalling graphviz-0.10.1:\n",
      "      Successfully uninstalled graphviz-0.10.1\n",
      "Successfully installed graphviz-0.8.4 mxnet-1.7.0.post1\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade mxnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 151648,
     "status": "ok",
     "timestamp": 1598793456283,
     "user": {
      "displayName": "Srinivas Chilukuri",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi6umIgrfUrIhlzS_2MLjaQhJAqRigLHF92McBw=s64",
      "userId": "11563496605008272618"
     },
     "user_tz": 300
    },
    "id": "QU3li7LQm524",
    "outputId": "48c0d5c5-28ac-482f-a6f9-55c9e9d9f7d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting autogluon\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from autogluon) (1.18.5)\n",
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      "Collecting fastparquet==0.4.1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/28/b9/844e32d0e3739e5695057dff3a3b9f4abc0fcccff466fdaadb8fedb0ee1d/fastparquet-0.4.1.tar.gz (28.6MB)\n",
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      "Collecting Pillow<=6.2.1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5c/0e94e689de2476c4c5e644a3bd223a1c1b9e2bdb7c510191750be74fa786/Pillow-6.2.1-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
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      "\u001b[?25hCollecting gluoncv<1.0,>=0.5.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/d7/74b530c461ac3eb90f6045a645a59450de1f3d616a4926e371daa021dbd8/gluoncv-0.8.0-py2.py3-none-any.whl (810kB)\n",
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      "\u001b[?25hCollecting cryptography>=2.8\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c0/9c/647e559a6e8be493dc2a7a5d15d26cb501ca60ec299b356f23839a673a83/cryptography-3.1-cp35-abi3-manylinux2010_x86_64.whl (2.6MB)\n",
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      "Collecting ConfigSpace<=0.4.10\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/42/de/4e8e4f26332fc65404f52baa112defbf822b6738b60bfa6b2993f5c60933/ConfigSpace-0.4.10.tar.gz (882kB)\n",
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      "\u001b[?25hCollecting lightgbm<3.0,>=2.3.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0b/9d/ddcb2f43aca194987f1a99e27edf41cf9bc39ea750c3371c2a62698c509a/lightgbm-2.3.1-py2.py3-none-manylinux1_x86_64.whl (1.2MB)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/df/fa46d8077b2821aba65a7d832eb6ee86fc67c5f180dc889144e96f45c0c4/autogluon_contrib_nlp-0.0.1b20200815-py3-none-any.whl (147kB)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/ce/81c45382fae736dc2b0a56358b117800939f3dab51933782c4c340a772de/distributed-2.25.0-py3-none-any.whl (652kB)\n",
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      "Collecting scikit-optimize\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/87/310b52debfbc0cb79764e5770fa3f5c18f6f0754809ea9e2fc185e1b67d3/scikit_optimize-0.7.4-py2.py3-none-any.whl (80kB)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/3f/f6a428599e0d4497e1595030965b5ba455fd8ade6e977e3c819973c4b41d/pandas-0.25.3-cp36-cp36m-manylinux1_x86_64.whl (10.4MB)\n",
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      "Collecting portalocker\n",
      "  Downloading https://files.pythonhosted.org/packages/89/a6/3814b7107e0788040870e8825eebf214d72166adf656ba7d4bf14759a06a/portalocker-2.0.0-py2.py3-none-any.whl\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/05/d9/6eebe19d46bd05360c9a9aae822e67a80f9242aabbfc58b641b957546607/typing-3.7.4.3.tar.gz (78kB)\n",
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      "  Downloading https://files.pythonhosted.org/packages/e7/e3/898487e5dbeb612054cf2e0c188463acb358167fef749c53c8bb8918cea1/cloudpickle-1.6.0-py3-none-any.whl\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9d/57/2f5e6226a674b2bcb6db531e8b383079b678df5b10cdaa610d6cf20d77ba/PyNaCl-1.4.0-cp35-abi3-manylinux1_x86_64.whl (961kB)\n",
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      "\u001b[?25hCollecting bcrypt>=3.1.3\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/70/6d218afbe4c73538053c1016dd631e8f25fffc10cd01f5c272d7acf3c03d/bcrypt-3.2.0-cp36-abi3-manylinux2010_x86_64.whl (63kB)\n",
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      "\u001b[?25hCollecting pyaml>=16.9\n",
      "  Downloading https://files.pythonhosted.org/packages/15/c4/1310a054d33abc318426a956e7d6df0df76a6ddfa9c66f6310274fb75d42/pyaml-20.4.0-py2.py3-none-any.whl\n",
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      "Collecting liac-arff>=2.4.0\n",
      "  Downloading https://files.pythonhosted.org/packages/e9/35/fbc9217cfa91d98888b43e1a19c03a50d716108c58494c558c65e308f372/liac-arff-2.4.0.tar.gz\n",
      "Collecting xmltodict\n",
      "  Downloading https://files.pythonhosted.org/packages/28/fd/30d5c1d3ac29ce229f6bdc40bbc20b28f716e8b363140c26eff19122d8a5/xmltodict-0.12.0-py2.py3-none-any.whl\n",
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      "Collecting mccabe<0.7.0,>=0.6.0\n",
      "  Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5b/88879fb861ab79aef45c7e199cae3ef7af487b5603dcb363517a50602dd7/pycodestyle-2.6.0-py2.py3-none-any.whl (41kB)\n",
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      "\u001b[?25hCollecting pyflakes<2.3.0,>=2.2.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/5b/fd01b0c696f2f9a6d2c839883b642493b431f28fa32b29abc465ef675473/pyflakes-2.2.0-py2.py3-none-any.whl (66kB)\n",
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      "Collecting immutables>=0.9\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB)\n",
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      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->flake8->autogluon-contrib-nlp->autogluon) (3.1.0)\n",
      "Building wheels for collected packages: fastparquet, ConfigSpace, openml, thrift, typing, sacremoses, contextvars, liac-arff\n",
      "  Building wheel for fastparquet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "  Stored in directory: /root/.cache/pip/wheels/10/45/cf/492ccb908adde1dd2551bb509a56e4096cce9487167f525120\n",
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      "  Stored in directory: /root/.cache/pip/wheels/75/83/cb/28dd42bac69c8867d485138030daa83841c7f84afe68b2fdf7\n",
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      "  Stored in directory: /root/.cache/pip/wheels/71/ec/5f/aaad9e184680b0b8f1a02ff0ec640cace5adf5bff7bb0af1b4\n",
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      "  Created wheel for thrift: filename=thrift-0.13.0-cp36-cp36m-linux_x86_64.whl size=345245 sha256=01c784871e2f76d9abbf8c00e53fb423e65a75dbcd69da798327157d3f5de120\n",
      "  Stored in directory: /root/.cache/pip/wheels/02/a2/46/689ccfcf40155c23edc7cdbd9de488611c8fdf49ff34b1706e\n",
      "  Building wheel for typing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "  Stored in directory: /root/.cache/pip/wheels/2d/04/41/8e1836e79581989c22eebac3f4e70aaac9af07b0908da173be\n",
      "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=9e4630d0217085e8cbb92b607c376cc2d693962379e6d0125b700a86d99a524d\n",
      "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
      "  Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for contextvars: filename=contextvars-2.4-cp36-none-any.whl size=7666 sha256=03a63429000dcb0d96e021bc70b356b9d5a4df56a1230558d25d74bfca26c8c6\n",
      "  Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de\n",
      "  Building wheel for liac-arff (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for liac-arff: filename=liac_arff-2.4.0-cp36-none-any.whl size=13335 sha256=b15c9a0ae3176a81cb4fac244af6541695a559063174442533e2b1c52efd4a73\n",
      "  Stored in directory: /root/.cache/pip/wheels/d1/6a/e7/529dc54d76ecede4346164a09ae3168df358945612710f5203\n",
      "Successfully built fastparquet ConfigSpace openml thrift typing sacremoses contextvars liac-arff\n",
      "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement cloudpickle==1.3, but you'll have cloudpickle 1.6.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: gym 0.17.2 has requirement cloudpickle<1.4.0,>=1.2.0, but you'll have cloudpickle 1.6.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.0.0; python_version >= \"3.0\", but you'll have pandas 0.25.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
      "Installing collected packages: pandas, thrift, fastparquet, Pillow, portalocker, gluoncv, cryptography, typing, ConfigSpace, lightgbm, sacremoses, sacrebleu, mccabe, pycodestyle, pyflakes, flake8, sentencepiece, immutables, contextvars, tokenizers, yacs, autogluon-contrib-nlp, cloudpickle, distributed, pynacl, bcrypt, paramiko, pyaml, scikit-optimize, catboost, liac-arff, xmltodict, openml, autogluon\n",
      "  Found existing installation: pandas 1.0.5\n",
      "    Uninstalling pandas-1.0.5:\n",
      "      Successfully uninstalled pandas-1.0.5\n",
      "  Found existing installation: Pillow 7.0.0\n",
      "    Uninstalling Pillow-7.0.0:\n",
      "      Successfully uninstalled Pillow-7.0.0\n",
      "  Found existing installation: lightgbm 2.2.3\n",
      "    Uninstalling lightgbm-2.2.3:\n",
      "      Successfully uninstalled lightgbm-2.2.3\n",
      "  Found existing installation: cloudpickle 1.3.0\n",
      "    Uninstalling cloudpickle-1.3.0:\n",
      "      Successfully uninstalled cloudpickle-1.3.0\n",
      "  Found existing installation: distributed 1.25.3\n",
      "    Uninstalling distributed-1.25.3:\n",
      "      Successfully uninstalled distributed-1.25.3\n",
      "Successfully installed ConfigSpace-0.4.10 Pillow-6.2.1 autogluon-0.0.13 autogluon-contrib-nlp-0.0.1b20200815 bcrypt-3.2.0 catboost-0.23.2 cloudpickle-1.6.0 contextvars-2.4 cryptography-3.1 distributed-2.25.0 fastparquet-0.4.1 flake8-3.8.3 gluoncv-0.8.0 immutables-0.14 liac-arff-2.4.0 lightgbm-2.3.1 mccabe-0.6.1 openml-0.10.2 pandas-0.25.3 paramiko-2.7.1 portalocker-2.0.0 pyaml-20.4.0 pycodestyle-2.6.0 pyflakes-2.2.0 pynacl-1.4.0 sacrebleu-1.4.13 sacremoses-0.0.43 scikit-optimize-0.7.4 sentencepiece-0.1.91 thrift-0.13.0 tokenizers-0.8.1 typing-3.7.4.3 xmltodict-0.12.0 yacs-0.1.8\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "PIL",
         "pandas",
         "typing"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pip install autogluon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 155621,
     "status": "ok",
     "timestamp": 1598793460267,
     "user": {
      "displayName": "Srinivas Chilukuri",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi6umIgrfUrIhlzS_2MLjaQhJAqRigLHF92McBw=s64",
      "userId": "11563496605008272618"
     },
     "user_tz": 300
    },
    "id": "SNp0SA3Nm9vK",
    "outputId": "ae876c4f-8ccb-411a-fea4-7ec57bf3687a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ipykernel\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/19/c2812690d8b340987eecd2cbc18549b1d130b94c5d97fcbe49f5f8710edf/ipykernel-5.3.4-py3-none-any.whl (120kB)\n",
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      "\u001b[?25hRequirement already satisfied, skipping upgrade: traitlets>=4.1.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel) (4.3.3)\n",
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      "Requirement already satisfied, skipping upgrade: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.1.0->ipykernel) (0.2.0)\n",
      "Requirement already satisfied, skipping upgrade: decorator in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.1.0->ipykernel) (4.4.2)\n",
      "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.1.0->ipykernel) (1.15.0)\n",
      "Requirement already satisfied, skipping upgrade: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (0.7.5)\n",
      "Requirement already satisfied, skipping upgrade: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (1.0.18)\n",
      "Requirement already satisfied, skipping upgrade: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (4.8.0)\n",
      "Requirement already satisfied, skipping upgrade: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (0.8.1)\n",
      "Requirement already satisfied, skipping upgrade: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (2.1.3)\n",
      "Requirement already satisfied, skipping upgrade: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.0.0->ipykernel) (49.6.0)\n",
      "Requirement already satisfied, skipping upgrade: jupyter-core>=4.6.0 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel) (4.6.3)\n",
      "Requirement already satisfied, skipping upgrade: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel) (19.0.2)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=5.0.0->ipykernel) (0.2.5)\n",
      "Requirement already satisfied, skipping upgrade: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=5.0.0->ipykernel) (0.6.0)\n",
      "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.3.4 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.0.0; python_version >= \"3.0\", but you'll have pandas 0.25.3 which is incompatible.\u001b[0m\n",
      "Installing collected packages: ipykernel\n",
      "  Found existing installation: ipykernel 4.10.1\n",
      "    Uninstalling ipykernel-4.10.1:\n",
      "      Successfully uninstalled ipykernel-4.10.1\n",
      "Successfully installed ipykernel-5.3.4\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "ipykernel"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pip install -U ipykernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "svQxEf0bo6g9"
   },
   "source": [
    "Restart Colab Runtime, then execute remaining cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ttBU5-Wyp3fp"
   },
   "source": [
    "# Start coding -- Load data, then train with AutoGluon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "9944a9af547540518268065326bb73a7",
      "1bed31105e8048b68ca509d1b4766ddd",
      "d23eb8cafb7e4ccb8decabde1bffecaa",
      "2c1f495e092b449c98b5d4cced5ac195",
      "d7fa13c501884ce7af7942dae4192e4b",
      "8b332827d2e448aaa20a6d499708fca9",
      "0f5a63e06933452d965a2663d56b90e6",
      "aab3ea920dfe44f5bcedc3c3a2e9b417",
      "b00523f134fd492b81313dd97d47b5d4",
      "79ab7e916a2846f9bee3e4400c80b1a5",
      "f0ccb28039184a0d97aa80784a55bf24",
      "2ff69f4eb61d483a9baa34c894e39d37",
      "0f89a84f7ce74e378a64534cb493524c",
      "fbeed41f9ed24f4cbf398dfa0cc56b02",
      "aa97e70b2f0b477889f5e577f52d1c46",
      "4279c3cdeab14e7e81366fec3d4f28e9"
     ]
    },
    "colab_type": "code",
    "collapsed": true,
    "executionInfo": {
     "elapsed": 2282862,
     "status": "ok",
     "timestamp": 1598824285781,
     "user": {
      "displayName": "Srinivas Chilukuri",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi6umIgrfUrIhlzS_2MLjaQhJAqRigLHF92McBw=s64",
      "userId": "11563496605008272618"
     },
     "user_tz": 300
    },
    "id": "0euz_DVgo7z5",
    "outputId": "413864ea-f882-4e80-a6e7-e2a252bef999"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: feature_prune does not currently work, setting to False.\n",
      "Warning: `hyperparameter_tune=True` is currently experimental and may cause the process to hang. Setting `auto_stack=True` instead is recommended to achieve maximum quality models.\n",
      "No output_directory specified. Models will be saved in: AutogluonModels/ag-20200830_211324/\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to AutogluonModels/ag-20200830_211324/\n",
      "AutoGluon Version:  0.0.13\n",
      "Train Data Rows:    1763\n",
      "Train Data Columns: 1563\n",
      "Preprocessing data ...\n",
      "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
      "Train Data Class Count: 2\n",
      "Feature Generator processed 1763 data points with 1526 features\n",
      "Original Features (raw dtypes):\n",
      "\tfloat64 features: 1526\n",
      "Original Features (inferred dtypes):\n",
      "\tfloat features: 1526\n",
      "Generated Features (special dtypes):\n",
      "Processed Features (raw dtypes):\n",
      "\tfloat features: 1526\n",
      "Processed Features:\n",
      "\tfloat features: 1526\n",
      "\tData preprocessing and feature engineering runtime = 2.62s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: roc_auc\n",
      "This metric expects predicted probabilities rather than predicted class labels, so you'll need to use predict_proba() instead of predict()\n",
      "To change this, specify the eval_metric argument of fit()\n",
      "AutoGluon will early stop models using evaluation metric: roc_auc\n",
      "Excluded Model Types: ['NN']\n",
      "\tFound 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.\n",
      "\t0.9119\t = Validation roc_auc score\n",
      "\t2.29s\t = Training runtime\n",
      "\t0.45s\t = Validation runtime\n",
      "\t0.9135\t = Validation roc_auc score\n",
      "\t2.28s\t = Training runtime\n",
      "\t0.44s\t = Validation runtime\n",
      "\t0.8526\t = Validation roc_auc score\n",
      "\t2.37s\t = Training runtime\n",
      "\t0.45s\t = Validation runtime\n",
      "\t0.8588\t = Validation roc_auc score\n",
      "\t2.38s\t = Training runtime\n",
      "\t0.45s\t = Validation runtime\n",
      "\t0.791\t = Validation roc_auc score\n",
      "\t0.2s\t = Training runtime\n",
      "\t0.41s\t = Validation runtime\n",
      "\t0.7964\t = Validation roc_auc score\n",
      "\t0.28s\t = Training runtime\n",
      "\t0.41s\t = Validation runtime\n",
      "scheduler_options: Key 'training_history_callback_delta_secs': Imputing default value 60\n",
      "scheduler_options: Key 'delay_get_config': Imputing default value True\n",
      "\n",
      "search_options: Key 'random_seed': Imputing default value 31415927\n",
      "search_options: Key 'opt_skip_init_length': Imputing default value 150\n",
      "search_options: Key 'opt_skip_period': Imputing default value 1\n",
      "search_options: Key 'profiler': Imputing default value False\n",
      "search_options: Key 'opt_maxiter': Imputing default value 50\n",
      "search_options: Key 'opt_nstarts': Imputing default value 2\n",
      "search_options: Key 'opt_warmstart': Imputing default value False\n",
      "search_options: Key 'opt_verbose': Imputing default value False\n",
      "search_options: Key 'opt_debug_writer': Imputing default value False\n",
      "search_options: Key 'num_fantasy_samples': Imputing default value 20\n",
      "search_options: Key 'num_init_random': Imputing default value 3\n",
      "search_options: Key 'num_init_candidates': Imputing default value 250\n",
      "search_options: Key 'initial_scoring': Imputing default value thompson_indep\n",
      "search_options: Key 'first_is_default': Imputing default value True\n",
      "search_options: Key 'debug_log': Imputing default value False\n",
      "\n",
      "Starting Experiments\n",
      "Num of Finished Tasks is 0\n",
      "Num of Pending Tasks is 1000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9944a9af547540518268065326bb73a7",
       "version_major": 2,
       "version_minor": 0
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       "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time out (secs) is 61.714285714285715\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.12558536]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.10674755]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.0850894]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.08061941]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07354193]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07354193]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07354193]\n",
      "\tRan out of time, early stopping on iteration 19. Best iteration is:\n",
      "\t[9]\ttrain_set's binary_logloss: 0.123625\ttrain_set's auc: 0.977989\tvalid_set's binary_logloss: 0.175262\tvalid_set's auc: 0.932046\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07354193]\n",
      "\t0.9251\t = Validation roc_auc score\n",
      "\t2.15s\t = Training runtime\n",
      "\t0.49s\t = Validation runtime\n",
      "\t0.9291\t = Validation roc_auc score\n",
      "\t2.22s\t = Training runtime\n",
      "\t0.49s\t = Validation runtime\n",
      "\t0.9201\t = Validation roc_auc score\n",
      "\t2.96s\t = Training runtime\n",
      "\t0.48s\t = Validation runtime\n",
      "\t0.9051\t = Validation roc_auc score\n",
      "\t4.79s\t = Training runtime\n",
      "\t0.49s\t = Validation runtime\n",
      "\t0.9163\t = Validation roc_auc score\n",
      "\t3.35s\t = Training runtime\n",
      "\t0.48s\t = Validation runtime\n",
      "\t0.9393\t = Validation roc_auc score\n",
      "\t1.94s\t = Training runtime\n",
      "\t0.5s\t = Validation runtime\n",
      "\t0.9085\t = Validation roc_auc score\n",
      "\t9.9s\t = Training runtime\n",
      "\t0.52s\t = Validation runtime\n",
      "\t0.9265\t = Validation roc_auc score\n",
      "\t2.27s\t = Training runtime\n",
      "\t0.5s\t = Validation runtime\n",
      "\t0.9222\t = Validation roc_auc score\n",
      "\t2.45s\t = Training runtime\n",
      "\t0.5s\t = Validation runtime\n",
      "\t0.9254\t = Validation roc_auc score\n",
      "\t3.0s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "\t0.92\t = Validation roc_auc score\n",
      "\t2.98s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "\t0.9292\t = Validation roc_auc score\n",
      "\t1.47s\t = Training runtime\n",
      "\t0.5s\t = Validation runtime\n",
      "\t0.9359\t = Validation roc_auc score\n",
      "\t1.93s\t = Training runtime\n",
      "\t0.53s\t = Validation runtime\n",
      "\t0.9388\t = Validation roc_auc score\n",
      "\t1.44s\t = Training runtime\n",
      "\t0.49s\t = Validation runtime\n",
      "\t0.932\t = Validation roc_auc score\n",
      "\t0.67s\t = Training runtime\n",
      "\t0.49s\t = Validation runtime\n",
      "scheduler_options: Key 'training_history_callback_delta_secs': Imputing default value 60\n",
      "scheduler_options: Key 'delay_get_config': Imputing default value True\n",
      "\n",
      "search_options: Key 'random_seed': Imputing default value 31415927\n",
      "search_options: Key 'opt_skip_init_length': Imputing default value 150\n",
      "search_options: Key 'opt_skip_period': Imputing default value 1\n",
      "search_options: Key 'profiler': Imputing default value False\n",
      "search_options: Key 'opt_maxiter': Imputing default value 50\n",
      "search_options: Key 'opt_nstarts': Imputing default value 2\n",
      "search_options: Key 'opt_warmstart': Imputing default value False\n",
      "search_options: Key 'opt_verbose': Imputing default value False\n",
      "search_options: Key 'opt_debug_writer': Imputing default value False\n",
      "search_options: Key 'num_fantasy_samples': Imputing default value 20\n",
      "search_options: Key 'num_init_random': Imputing default value 3\n",
      "search_options: Key 'num_init_candidates': Imputing default value 250\n",
      "search_options: Key 'initial_scoring': Imputing default value thompson_indep\n",
      "search_options: Key 'first_is_default': Imputing default value True\n",
      "search_options: Key 'debug_log': Imputing default value False\n",
      "\n",
      "Starting Experiments\n",
      "Num of Finished Tasks is 0\n",
      "Num of Pending Tasks is 1000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time out (secs) is 61.714285714285715\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.0813112]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "Fitting GP model\n",
      "BO Algorithm: Generating initial candidates.\n",
      "BO Algorithm: Scoring (and reordering) candidates.\n",
      "BO Algorithm: Selecting final set of candidates.\n",
      "Current best is [0.07992763]\n",
      "\t0.9185\t = Validation roc_auc score\n",
      "\t5.18s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "\t0.915\t = Validation roc_auc score\n",
      "\t5.58s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "\t0.9187\t = Validation roc_auc score\n",
      "\t5.8s\t = Training runtime\n",
      "\t0.53s\t = Validation runtime\n",
      "\t0.9201\t = Validation roc_auc score\n",
      "\t4.76s\t = Training runtime\n",
      "\t0.52s\t = Validation runtime\n",
      "\t0.9159\t = Validation roc_auc score\n",
      "\t7.3s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "\t0.9051\t = Validation roc_auc score\n",
      "\t4.73s\t = Training runtime\n",
      "\t0.52s\t = Validation runtime\n",
      "\t0.9115\t = Validation roc_auc score\n",
      "\t5.37s\t = Training runtime\n",
      "\t0.52s\t = Validation runtime\n",
      "\t0.9177\t = Validation roc_auc score\n",
      "\t9.76s\t = Training runtime\n",
      "\t0.5s\t = Validation runtime\n",
      "\t0.9088\t = Validation roc_auc score\n",
      "\t3.72s\t = Training runtime\n",
      "\t0.51s\t = Validation runtime\n",
      "Fitting model: RandomForestClassifierGini_STACKER_l0/0 ...\n",
      "\t0.9142\t = Validation roc_auc score\n",
      "\t56.2s\t = Training runtime\n",
      "\t12.55s\t = Validation runtime\n",
      "Fitting model: RandomForestClassifierEntr_STACKER_l0/0 ...\n",
      "\t0.9112\t = Validation roc_auc score\n",
      "\t55.48s\t = Training runtime\n",
      "\t13.11s\t = Validation runtime\n",
      "Fitting model: ExtraTreesClassifierGini_STACKER_l0/0 ...\n",
      "\t0.8718\t = Validation roc_auc score\n",
      "\t62.1s\t = Training runtime\n",
      "\t12.86s\t = Validation runtime\n",
      "Fitting model: ExtraTreesClassifierEntr_STACKER_l0/0 ...\n",
      "\t0.8693\t = Validation roc_auc score\n",
      "\t60.41s\t = Training runtime\n",
      "\t13.02s\t = Validation runtime\n",
      "Fitting model: KNeighborsClassifierUnif_STACKER_l0/0 ...\n",
      "\t0.8708\t = Validation roc_auc score\n",
      "\t5.95s\t = Training runtime\n",
      "\t10.36s\t = Validation runtime\n",
      "Fitting model: KNeighborsClassifierDist_STACKER_l0/0 ...\n",
      "\t0.8418\t = Validation roc_auc score\n",
      "\t6.07s\t = Training runtime\n",
      "\t10.25s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/0 ...\n",
      "\t0.9132\t = Validation roc_auc score\n",
      "\t34.15s\t = Training runtime\n",
      "\t9.04s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/1 ...\n",
      "\t0.9208\t = Validation roc_auc score\n",
      "\t33.46s\t = Training runtime\n",
      "\t8.97s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/2 ...\n",
      "\t0.8963\t = Validation roc_auc score\n",
      "\t40.53s\t = Training runtime\n",
      "\t9.47s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/3 ...\n",
      "\t0.8887\t = Validation roc_auc score\n",
      "\t64.75s\t = Training runtime\n",
      "\t9.02s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/4 ...\n",
      "\t0.9038\t = Validation roc_auc score\n",
      "\t50.55s\t = Training runtime\n",
      "\t9.09s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/5 ...\n",
      "\t0.9123\t = Validation roc_auc score\n",
      "\t35.92s\t = Training runtime\n",
      "\t9.15s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/6 ...\n",
      "\t0.8853\t = Validation roc_auc score\n",
      "\t79.59s\t = Training runtime\n",
      "\t9.18s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/7 ...\n",
      "\t0.894\t = Validation roc_auc score\n",
      "\t54.78s\t = Training runtime\n",
      "\t9.23s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/8 ...\n",
      "\t0.901\t = Validation roc_auc score\n",
      "\t49.28s\t = Training runtime\n",
      "\t9.14s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/9 ...\n",
      "\t0.9032\t = Validation roc_auc score\n",
      "\t39.63s\t = Training runtime\n",
      "\t9.08s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/10 ...\n",
      "\t0.901\t = Validation roc_auc score\n",
      "\t42.53s\t = Training runtime\n",
      "\t9.19s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/11 ...\n",
      "\t0.9086\t = Validation roc_auc score\n",
      "\t30.64s\t = Training runtime\n",
      "\t9.01s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/12 ...\n",
      "\t0.9027\t = Validation roc_auc score\n",
      "\t35.32s\t = Training runtime\n",
      "\t9.15s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/13 ...\n",
      "\t0.9094\t = Validation roc_auc score\n",
      "\t32.9s\t = Training runtime\n",
      "\t9.07s\t = Validation runtime\n",
      "Fitting model: LightGBMClassifier_STACKER_l0/14 ...\n",
      "\t0.9177\t = Validation roc_auc score\n",
      "\t32.31s\t = Training runtime\n",
      "\t9.08s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/0 ...\n",
      "\t0.9016\t = Validation roc_auc score\n",
      "\t85.28s\t = Training runtime\n",
      "\t9.43s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/1 ...\n",
      "\t0.8993\t = Validation roc_auc score\n",
      "\t96.09s\t = Training runtime\n",
      "\t9.41s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/2 ...\n",
      "\t0.8787\t = Validation roc_auc score\n",
      "\t101.76s\t = Training runtime\n",
      "\t9.4s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/3 ...\n",
      "\t0.8734\t = Validation roc_auc score\n",
      "\t82.33s\t = Training runtime\n",
      "\t9.5s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/4 ...\n",
      "\t0.8807\t = Validation roc_auc score\n",
      "\t112.98s\t = Training runtime\n",
      "\t9.46s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/5 ...\n",
      "\t0.8546\t = Validation roc_auc score\n",
      "\t84.91s\t = Training runtime\n",
      "\t9.34s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/6 ...\n",
      "\t0.852\t = Validation roc_auc score\n",
      "\t109.87s\t = Training runtime\n",
      "\t9.65s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/7 ...\n",
      "\t0.8741\t = Validation roc_auc score\n",
      "\t223.96s\t = Training runtime\n",
      "\t9.32s\t = Validation runtime\n",
      "Fitting model: CatboostClassifier_STACKER_l0/8 ...\n",
      "\t0.842\t = Validation roc_auc score\n",
      "\t130.34s\t = Training runtime\n",
      "\t9.52s\t = Validation runtime\n",
      "Fitting model: weighted_ensemble_k0_l1 ...\n",
      "\t0.9314\t = Validation roc_auc score\n",
      "\t2.8s\t = Training runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 2282.02s ...\n"
     ]
    }
   ],
   "source": [
    "from autogluon import TabularPrediction as task\n",
    "import pandas as pd\n",
    "\n",
    "### Point to the MH18_Train2.csv file that is generated using \n",
    "train = pd.read_csv('/content/drive/My Drive/Colab Notebooks/MH18_Train2.csv')\n",
    "\n",
    "train_data = task.Dataset(train)\n",
    "\n",
    "predictor = task.fit(train_data=train_data,\n",
    "                     label='Class',\n",
    "                     problem_type='binary', \n",
    "                     eval_metric='roc_auc',\n",
    "                     stopping_metric='roc_auc',\n",
    "                     num_bagging_folds=5,\n",
    "                     hyperparameter_tune=True,\n",
    "                     num_bagging_sets=5,\n",
    "                     random_seed=0)"
   ]
  },
  {
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     },
     "execution_count": 14,
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   "source": [
    "### Point this to the MH18_Test2.csv that is generated using MH18-Notebook-1\n",
    "test = pd.read_csv('/content/drive/My Drive/Colab Notebooks/MH18_Test2.csv')\n",
    "\n",
    "test_data = task.Dataset(test)\n",
    "\n",
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